Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-23T08:43:10.525Z Has data issue: false hasContentIssue false
  • English
  • Français

Parametric Modeling and Simulation of Joint Price-Production Distributions under Non-Normality, Autocorrelation and Heteroscedasticity: A Tool for Assessing Risk in Agriculture

Published online by Cambridge University Press:  28 April 2015

Octavio A. Ramirez
Octavio A. Ramirez*
Affiliation:
Department of Agricultural and Applied Economics, Texas Tech University, Lubbock, Texas
Get access Rights & Permissions [Opens in a new window]

Abstract

This study presents a way to parametrically model and simulate multivariate distributions under potential non-normality, autocorrelation and heteroscedasticity and illustrates its application to agricultural risk analysis. Specifically, the joint probability distribution (pdf) for West Texas irrigated cotton, corn, sorghum, and wheat production and prices is estimated and applied to evaluate the changes in the risk and returns of agricultural production in the region resulting from observed and predicted price and production trends. The estimated pdf allows for time trends on the mean and the variance and varying degrees of autocorrelation and non-normality (kurtosis and right- or left-skewness) in each of the price and production variables. It also allows for any possible price-price, production-production, or price-production correlation.

Keywords

agricultural risk analysisautocorrelationheteroscedasticitymultivariate non-normal simulationWest Texas agriculture
Type
Invited Paper Sessions
Information
Journal of Agricultural and Applied Economics , Volume 32 , Issue 2 , August 2000 , pp. 283 - 297
Copyright
Copyright © Southern Agricultural Economics Association 2000

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J.R. “Simulation: Methodology and Application in Agricultural Economics.” Rev. Mark. Agrie. Econ. 42(1974):3–55.Google Scholar
Clements, A.M., Mapp, H.P Jr, and Eidman, V.R.. “A Procedure for Correlating Events in Farm Firm Simulation Models.” Oklahoma State Univ. Agrie. Experiment Station Bulletin T-131, 1971.Google Scholar
Gallagher, P. “U.S. Soybean Yields: Estimation and Forecasting with Nonsymmetric Disturbances.” Amer. J. Agrie. Econ. 71(November 1987):796–803.CrossRefGoogle Scholar
Judge, G.G., Griffiths, W.E., Carter Hill, R., Lutkepohl, H., and Lee, Tsoung-Chao. The Theory and Practice of Econometrics. New York: John Wiley & Sons, Inc., 1985, pp. 419–464.Google Scholar
Meyer, J. “Choice among distributions”. J. of Econ. Theory, 14(1977):326–336.CrossRefGoogle Scholar
Mood, A.M., Graybill, F.A. and Boes, D.C.. Introduction to the Theory of Statistics. New York: McGraw-Hill, 1974, pp. 198–212.Google Scholar
Ramirez, O.A. “Estimation and use of a multivariate parametric model for simulating heteros-cedastic, correlated, non-normal random variables: the case of corn-belt corn, soybeans and wheat yields”. Amer. J. Agr. Econ., 79(Febraary 1997):191–205.CrossRefGoogle Scholar
Ramirez, O.A., Moss, C.B. and Boggess, W.G.. “Estimation and use of the inverse hyperbolic sine transformation to model non-normal correlates random variables”. J. App. Stat., 21(December 1994):289–304.CrossRefGoogle Scholar
Ramirez, O.A. and Somarriba, E.. “Modeling and simulation of autocorrelated non-normal time series for agricultural risk analysis”. Texas Tech Univ., CASNR Manuscript No. T-1-487.Google Scholar
Ramirez, O.A. and Sosa, R.. “Assessing the financial risks of diversified coffee production systems: an alternative non-normal CDF estimation approach.” J. Agrie. Res. Econ. (forthcoming).Google Scholar
Roy, A.D. “Safety-first and the holding of assets. Econometrica, 20(1952):431–49.CrossRefGoogle Scholar
Richardson, J.W and Condra, G.D.. “A General Procedure for Correlating Events in Simulation Models.” Department of Agricultural Economics, Texas A&M University, 1978.Google Scholar
Taylor, C.R. “Two Practical Procedures for Estimating Multivariate Nonnormal Probability Density Functions.” Amer. J. Agrie. Econ. 72(February 1990):210–217.CrossRefGoogle Scholar
Texas Agricultural Statistics. Texas Agrie. Stat. Service (TASS), 1969-1998 yearbooks.Google Scholar
United States Department of Agriculture/National Agricultural Statistics Service (USDA/NASS). USDA/NASS Internet Site, October of 1999.Google Scholar